Estimating obstacle materials from floor plans

Abstract

Estimating materials of obstacles from a floor plan (310) is described. A floor plan is loaded (210) and preprocessed (215, 410). A material map (510) for the preprocessed floor plan is generated using a machine learning model for estimating materials of obstacles of the floor plan (220, 225). An obstacle map (610) is generated from the preprocessed floor plan that includes segmented obstacles as line segments. An augmented floor plan (710) is generated (225) based on a combination of the generated material map and the generated obstacle map, where the augmented floor plan identifies the estimated material of the segmented obstacles.

1 . A method for estimating materials of obstacles from a floor plan, the method comprising:
 loading and preprocessing the floor plan;   generating a material map for the preprocessed floor plan using a machine learning model for estimating materials of obstacles of the floor plan;   generating an obstacle map from the preprocessed floor plan that includes segmented obstacles as line segments; and   generating, based on a combination of the generated material map and the generated obstacle map, an augmented floor plan that identifies the estimated material of the segmented obstacles.   
     
     
         2 . The method of  claim 1 , further comprising:
 converting the generated augmented floor plan to a format readable by a radio frequency (RF) design/simulation tool; and   providing the generated augmented floor plan to the RF design/simulation tool.   
     
     
         3 . (canceled) 
     
     
         4 . The method of  claim 1 , wherein preprocessing the floor plan includes setting one or more regions of interest. 
     
     
         5 . The method of  claim 1 , wherein preprocessing the floor plan includes removing noise and unrelated peripheral texts and lines. 
     
     
         6 . The method of  claim 1 , wherein the machine learning model is a U-Net model that is pretrained as a generator in a conditional generative adversarial network (cGAN), wherein the cGAN uses a function that considers losses on obstacles only when training the machine learning model such as a focal Tversky loss function, and wherein a discriminator of the cGAN uses a convolutional PatchGAN classifier. 
     
     
         7 . (canceled) 
     
     
         8 . (canceled) 
     
     
         9 . The method of  claim 1 , wherein the obstacles of the floor plan include one or more walls, one or more columns, and/or one or more fixtures. 
     
     
         10 . The method of  claim 1 , wherein the floor plan is a raster graphics image, and wherein generating the obstacle map includes vectorizing the preprocessed floor plan to the line segments. 
     
     
         11 . (canceled) 
     
     
         12 . (canceled) 
     
     
         13 . (canceled) 
     
     
         14 . (canceled) 
     
     
         15 . (canceled) 
     
     
         16 . (canceled) 
     
     
         17 . (canceled) 
     
     
         18 . A non-transitory computer-readable storage medium that provides instructions that, if executed by a processor, will cause said processor to perform operations for estimating materials of obstacles from a floor plan, the operations comprising:
 loading and preprocessing the floor plan;   generating a material map for the preprocessed floor plan using a machine learning model for estimating materials of obstacles of the floor plan;   generating an obstacle map-from the preprocessed floor plan that includes segmented obstacles as line segments; and   generating, based on a combination of the generated material map and the generated obstacle map, an augmented floor plan that identifies the estimated material of the segmented obstacles.   
     
     
         19 . The non-transitory computer-readable storage medium of  claim 18 , wherein the operations further comprise:
 converting the generated augmented floor plan to a format readable by a radio frequency (RF) design/simulation tool; and   providing the generated augmented floor plan to the RF design/simulation tool.   
     
     
         20 . The non-transitory computer-readable storage medium of  claim 18 , wherein preprocessing the floor plan includes setting one or more regions of interest. 
     
     
         21 . The non-transitory computer-readable storage medium of  claim 18 , wherein preprocessing the floor plan includes removing noise and unrelated peripheral texts and lines. 
     
     
         22 . The non-transitory computer-readable storage medium of  claim 18 , wherein the machine learning model is a U-Net model that is pretrained as a generator in a conditional generative adversarial network (cGAN), wherein the cGAN uses a function that considers losses on obstacles only when training the machine learning model such as a focal Tversky loss function, and wherein a discriminator of the cGAN uses a convolutional PatchGAN classifier. 
     
     
         23 . The non-transitory computer-readable storage medium of  claim 18 , wherein the obstacles of the floor plan include one or more walls, one or more columns, and/or one or more fixtures. 
     
     
         24 . The non-transitory computer-readable storage medium of  claim 18 , wherein the floor plan is a raster graphics image, and wherein generating the obstacle map includes vectorizing the preprocessed floor plan to the line segments. 
     
     
         25 . An electronic device for estimating materials of obstacles from a floor plan, the electronic device comprising:
 a processor; and   a non-transitory computer-readable storage medium that provides instructions that, if executed by the processor, cause the electronic device to perform operations including:
 loading and preprocessing the floor plan, 
 generating a material map for the preprocessed floor plan using a machine learning model-for estimating materials of obstacles of the floor plan, 
 generating an obstacle map-from the preprocessed floor plan that includes segmented obstacles as line segments, and 
 generating, based on a combination of the generated material map and the generated obstacle map, an augmented floor plan that identifies the estimated material of the segmented obstacles. 
   
     
     
         26 . The electronic device of  claim 25 , wherein the operations further comprise:
 converting the generated augmented floor plan to a format readable by a radio frequency (RF) design/simulation tool; and   providing the generated augmented floor plan to the RF design/simulation tool.   
     
     
         27 . The electronic device of  claim 25 , wherein preprocessing the floor plan includes setting one or more regions of interest. 
     
     
         28 . The electronic device of  claim 25 , wherein preprocessing the floor plan includes removing noise and unrelated peripheral texts and lines. 
     
     
         29 . The electronic device of  claim 25 , wherein the machine learning model is a U-Net model that is pretrained as a generator in a conditional generative adversarial network (cGAN), wherein the cGAN uses a function that considers losses on obstacles only when training the machine learning model such as a focal Tversky loss function, and wherein a discriminator of the cGAN uses a convolutional PatchGAN classifier. 
     
     
         30 . The electronic device of  claim 25 , wherein the obstacles of the floor plan include one or more walls, one or more columns, and/or one or more fixtures.